code: def forward_layer(self, layer, x, adj_t, state):
      if layer == 0 and self.drop_input:
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      h = self.convs[layer](x, adj_t)
      if layer < self.num_layers - 1:
          if self.batch_norm:
              h = self.bns[layer](h)
          if self.residual and h.size(-1) == x.size(-1):
              h += x[: h.size(0)]
          h = h.relu_()
          h = F.dropout(h, p=self.dropout, training=self.training)
  
      return h
  
imports: from itertools import product
  from typing import Optional, List
  import tqdm
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_sparse import SparseTensor
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import PNAConv
  from torch_scatter import segment_csr
  from ..models import ScalableGNN
label_desc: Use image node_id 6 for calling the PNA obj's underlying member method code with example usage: obj.forward_layer(layer, x, adj_t, state) and returns: h